Systems and methods for environment sensing

ABSTRACT

Systems and methods are provided for environment sensing. The system includes a sensor node having a sensor. The sensor includes a sensing material configured to be in contact with an ambient environment. The system includes a remote system having a communication circuit and a controller circuit. The communication circuit is configured to be wirelessly communicatively coupled to the sensor node. The controller circuit electrically coupled to the communication circuit. The controller circuit configured to receive an impedance response of the sensing material and analyze the impedance response of the sensing material at frequencies that provide a linear response of the sensing material to an analyte of interest and at least partially reject effects of interferences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/270,442, which was filed 20 Sep. 2016, and the entire disclosure ofwhich is incorporated herein by reference.

FIELD

Embodiments of the subject matter disclosed herein generally relate tosystems and methods for environment sensing.

BACKGROUND

Environmental sensors, such as metal-oxide sensors, are often employedin several applications where the detection of various vapors or gasesmay be used to discern useful information. For example, environmentalsensors may be utilized to monitor industrial areas for chemical orphysical hazards, such as the detection of carbon dioxide within thechemical manufacturing industry, detection of engine exhaust gases suchas carbon monoxide, hydrocarbons, and nitrogen oxides within thetransportation industry, and detection of fugitive methane emissionswithin the oil and gas industry.

One technique for sensing such environmental changes is by employing aconventional sensor, such as a radio frequency identification (RFID)sensor, a resistance sensor, and/or a capacitance sensor coated with aparticular sensing material. The impedance, resistance, capacitanceresponse of the conventional sensor can be measured via inductivecoupling or directly by connecting to a sensor reader. The electricalresponse of the conventional sensor is translated into the impedance,resistance, or capacitance changes of the conventional sensor, which isutilized to determine a concentration of a chemical vapor of interest,such as carbon dioxide, carbon monoxide, and nitrogen oxide, or methanegas. However, available conventional sensors suffer from a non-linearresponse, specifically an exponential, power law, and/or non-monotonicresponse, as a function of the chemical vapor concentration. Due to thepower law response, as the concentration of the chemical vaporincreases, the chemical vapor saturates the conventional sensor responseleading to significant errors in estimation of the chemical vaporconcentration. The terms “gas” and “vapor” describe any volatile speciesthat are in contact with the sensor.

Additionally, conventional sensors are affected by other chemical vapors(e.g., not the chemical vapor of interest) exposed to the conventionalsensor, such as a concentration of water vapor (e.g., ambient humidity).The water vapor shifts or saturates the response of the conventionalsensor, which can affect a determination of the concentration of thechemical vapor of interest by the sensor.

The conventional sensors can be implemented in a conventional wirelesssensing network (WSN) as sensor nodes. However, the sensor nodes withinthe conventional WSN are unable to measure multiple gases withindividual sensors, reducing the reliability of the conventional WSN.Thus, conventional WSN require multiple conventional sensors for eachsensor node. Each conventional sensor is configured to measure aspecific gas. However, due to the plurality of conventional sensors forthe sensor nodes, the sensor nodes demand a high-power consumption,which restricts the type of power sources that can be utilized to powerthe node. Further, the high-power consumption reduces the lifetime ofthe sensor nodes within the conventional WSN.

BRIEF DESCRIPTION

In an embodiment a system is provided. The system includes a sensor nodehaving a sensor. The sensor includes a sensing material configured to bein contact with an ambient environment. The system includes a remotesystem having a communication circuit and a controller circuit. Thecommunication circuit is configured to be wirelessly communicativelycoupled to the sensor node. The controller circuit is electricallycoupled to the communication circuit. The controller circuit isconfigured to receive an impedance response of the sensing material andanalyze the impedance response of the sensing material at frequenciesthat provide a linear response of the sensing material to an analyte ofinterest and at least partially reject effects of interferences.

In an embodiment a sensor node is provided. The sensor node includes asensor having a sensing material and at least one pair of electrodes incontact with the sensing material. The sensing material is configured tobe in contact with an ambient environment. The sensor node includes acommunication circuit configured to be communicatively coupled to aremote system. The sensor node includes a controller circuitelectrically coupled to the at least one pair of electrodes. Thecontroller circuit is configured to generate a stimulation waveform forapplications to the sensing material of the sensor via the at least onepair of electrodes. The controller circuit is configured to receive anelectrical signal from the at least one pair of electrodesrepresentative of an impedance response of the sensing material. Thecontroller circuit is further configured to control the communicationcircuit to transmit the impedance response to the remote system.

In an embodiment a method (e.g., for detecting one or more analytes ofinterest) is provided. The method includes receiving a plurality ofimpedance responses and one or more ambient parameters from a pluralityof sensor nodes. Each impedance response is representative of a sensingmaterial of a sensor node in operational contact with an ambientenvironment. The method includes adjusting the plurality of impedanceresponses based on the one or more ambient parameters and analyzing theplurality of impedance responses at frequencies that provide a linearresponse of the sensing material to an analyte of interest and at leastpartially reject effects of interferences.

BRIEF DESCRIPTION OF THE DRAWINGS

The presently described subject matter will be better understood fromreading the following description of non-limiting embodiments, withreference to the attached drawings, wherein below:

FIG. 1 is a schematic diagram of one embodiment of a wireless sensingnetwork;

FIG. 2 is a schematic diagram of a sensor node of one embodiment of thewireless sensing network system of FIG. 1;

FIG. 3 is a schematic diagram of a remote system of one embodiment ofthe wireless sensing network system of FIG. 1;

FIG. 4 is a “swim lane” diagram of one embodiment of a method fordetecting one or more analytes of interest within a wireless sensornetwork;

FIG. 5 shows a graphical illustration of one embodiment of a stimulationwaveform applied to a sensing material of a sensor;

FIG. 6A shows graphical illustrations of a measured responsecorresponding to a non-resonance impedance response of a sensor, inaccordance with an embodiment;

FIG. 6B shows graphical illustrations of a measured responsecorresponding to a resonance impedance response of a sensor, inaccordance with an embodiment;

FIG. 7A is a graphical illustration of one embodiment of a spectralparameter calculated from a sensor;

FIG. 7B is a graphical illustration of one peak of an embodiment of thespectral parameter shown in the FIG. 7A;

FIG. 8A is a graphical illustration of one embodiment of a spectralparameter calculated from a sensor;

FIG. 8B is a graphical illustration of one peak of an embodiment of thespectral parameter shown in the FIG. 8A;

FIG. 9A is a graphical illustration of one embodiment of a spectralparameter calculated from a sensor;

FIG. 9B is a graphical illustration of a concentration curve of thesensor based on the spectral parameter shown in FIG. 9A;

FIG. 10 is a graphical illustration of a spectral parameter calculatedfrom an impedance response received by a remote system from a sensornode, in accordance with an embodiment;

FIG. 11 is a graphical illustration of one embodiment of a principalcomponent analysis of a plurality of spectral parameters;

FIG. 12 are graphical illustrations of an embodiment of an impedanceresponse and an ambient parameter of a sensor node of the wirelesssensing network system shown in FIG. 1;

FIG. 13A-B are graphical illustrations of impedance responses ofembodiments of the sensor nodes of the wireless sensing network systemshown in FIG. 1; and

FIG. 14 are graphical illustrations of an embodiment of an impedanceresponse and ambient parameters of a sensor node of the wireless sensingnetwork system shown in FIG. 1.

DETAILED DESCRIPTION

One or more embodiments herein describe systems and methods forenvironment sensing, specifically a wireless sensor network (WSN) havingsensor nodes configured to detect one or more analytes of interest(e.g., methane gas, carbon monoxide gas, nitrogen oxide gas) within anenvironment. The sensor nodes include a sensor, such as a multivariableanalyte sensor, and an environment sensor. The sensor may be similar toand/or the same as the sensor described in U.S. Patent Applicationentitled, “SYSTEMS AND METHODS FOR ENVIRONMENT SENSING” having docketnumber 285314-1US, which is incorporated by reference in its entirety.The environment sensor may be configured to acquire ambient parametersof the environment (e.g., not the analytes of interest), such as ambienttemperature, ambient relative humidity, ambient atmospheric pressure,meteorological conditions, light detection, wind direction, wind speed,and/or the like.

The sensor nodes are powered by an ambient power source (e.g., solarpanel, vibration, thermal power, ambient radio-frequency power, and/orthe like). The sensor utilizes a sensing material electrically coupledto a pair of electrodes. An electrical stimulus is delivered to thesensor that includes a sensing material. Optionally, the multivariableanalyte sensor may include a resonant inductor capacitor resistor (LCR)circuit and/or an RFID sensor.

An impedance response (e.g., impedance spectrum) of the sensor ismeasured via a controller circuit of the sensor node directly and/orinductive coupled between a pick up coil and the sensor. For example,the electrical response at certain frequencies or a single frequencycorresponding to signal changes (e.g., impedance, resistance,capacitance, and/or the like) of the sensor is translated into theimpedance changes of the sensor to form the impedance response. Based onthe impedance response, the controller circuit may calculate one or morespectrum parameters. The spectrum parameters are calculated from a realportion and/or imaginary portion of the impedance response. The“spectrum” or “spectral” parameters are utilized to determine anenvironmental parameter of the analytes of interest. For example, thecontroller circuit may analyze the impedance response of the sensingmaterial of the sensor at frequencies calculated from the real portionof the impedance response that provide a linear response of the sensingmaterial to determine the environmental parameters (e.g., concentration)of the analytes of interest. It may be noted, the impedance response ofthe sensing material described herein provides a linearity improvementover the nonlinear (e.g. power law) resistance response of the sensingmaterial in conventional environmental sensors. Additionally, due to thelinear response, the impedance response of the sensing material providesa monotonic response improvement over the non-monotonic resistanceresponse (e.g., parabolic) of the sensing material in conventionalenvironmental sensors. Additionally or alternatively, the spectrumparameters may be selected to reject and/or filter out effects ofinterference due to volatile analytes (e.g., analytes not of interest).For example, the impedance response of the sensing material providesreduction of effects of humidity over the resistance response of thesensing material in conventional environmental sensors.

The sensor node includes an RF circuit, which is configured to transmitthe environmental parameters of the analytes of interest and the ambientparameters acquired by the environmental sensor to a remote system(e.g., central hub, WSN gateway, and/or the like). Optionally, thesensor nodes may transmit the environmental and ambient parameters atpredetermined intervals. Additionally or alternatively, the remotesystem may receive additional ambient parameters from a remote weatherstation of the WSN.

The fluids described herein can include gases, vapors, liquids,particles, biological particles, and/or biological molecules.Optionally, a fluid may refer to one or more solid materials.

Each sensor node may have a digital identification or ID that caninclude data stored in a memory chip (or other memory device) of thesensor node. Non-limiting examples of this data include manufactureridentification, electronic pedigree data, user data, and/or calibrationdata for the sensor. Additionally or alternatively, the sensor node mayhave an IP address that may allow the sensor node connectivity to theInternet or other remote-based net, server, database, cloud or any othersource of remote data storage and processing.

A monitoring process includes, but is not limited to, measuring physicalchanges that occur around the sensor. For example, monitoring processesincluding monitoring changes in a biopharmaceutical, food or beveragemanufacturing process related to changes in physical, chemical, and/orbiological properties of an environment around the sensor. Monitoringprocesses may also include those industry processes that monitorphysical changes as well as changes in a component's composition orposition. Non-limiting examples include homeland security monitoring,residential home protection monitoring, environmental monitoring,clinical or bedside patient monitoring, airport security monitoring,admission ticketing, and other public events. Monitoring can beperformed when the sensor signal has reached an appreciably steady stateresponse and/or when the sensor has a dynamic response. The steady statesensor response is a response from the sensor over a determined periodof time, where the response does not appreciably change over themeasurement time. Thus, measurements of steady state sensor responseover time produce similar values. The dynamic sensor response is aresponse from the sensor upon a change in the measured environmentalparameter (temperature, pressure, chemical concentration, biologicalconcentration, etc.). Thus, the dynamic sensor response significantlychanges over the measurement time to produce a dynamic signature ofresponse toward the environmental parameter or parameters measured.Non-limiting examples of the dynamic signature of the response includeaverage response slope, average response magnitude, largest positiveslope of signal response, largest negative slope of signal response,average change in signal response, maximum positive change in signalresponse, and maximum negative change in signal response. The produceddynamic signature of response can be used to further enhance theselectivity of the sensor in dynamic measurements of individual vaporsand their mixtures. The produced dynamic signature of response can alsobe used to further optimize the combination of sensing material andtransducer geometry to enhance the selectivity of the sensor in dynamicand steady state measurements of individual vapors and their mixtures.

Environmental parameters and/or select parameters can refer tomeasurable environmental variables within or surrounding a manufacturingor monitoring system (e.g., a sensing system). The measurableenvironmental variables comprise at least one of physical, chemical, andbiological properties and include, but are not limited to, measurementof temperature, pressure, material concentration, conductivity,dielectric property, number of dielectric, metallic, chemical, orbiological particles in the proximity or in contact with the sensor,dose of ionizing radiation, and light intensity.

An analyte can include any desired measured environmental parameter.

Interference includes an undesired environmental parameter thatundesirably affects the accuracy and precision of measurements with thesensor. An interference includes a fluid or an environmental parameter(that includes, but is not limited to temperature, pressure, light,etc.) that potentially may produce an interference response by thesensor.

A multivariate analysis can refer to a mathematical procedure that isused to analyze more than one variable from the sensor response and toprovide the information about the type of at least one environmentalparameter from the measured sensor spectral parameters and/or toquantitative information about the level of at least one environmentalparameter from the measured sensor spectral parameters. A principalcomponent analysis (PCA) includes a mathematical procedure that is usedto reduce multidimensional data sets to lower dimensions for analysis.Principal component analysis is a part of eigenanalysis methods ofstatistical analysis of multivariate data and may be performed using acovariance matrix or correlation matrix. Non-limiting examples ofmultivariate analysis tools include canonical correlation analysis,regression analysis, nonlinear regression analysis, principal componentsanalysis, discriminate function analysis, multidimensional scaling,linear discriminate analysis, logistic regression, or neural networkanalysis.

Spectral parameters or spectrum parameters may be used to refer tomeasurable variables of the impedance response of the sensor. Theimpedance sensor response is the impedance spectrum of the non-resonancesensor circuit of the CR (capacitance (C) -resistance (R)) sensor. Theimpedance sensor response is the impedance spectrum of the resonancesensor circuit of the LCR (inductance (L)-capacitance (C)-resistance(R)) or RFID (radio-frequency identification) sensor. In addition tomeasuring the impedance spectrum in the form of Z-parameters,S-parameters, and other parameters, the impedance spectrum (both realand imaginary parts) may be analyzed simultaneously using variousparameters for analysis, such as, the frequency of the maximum of thereal part of the impedance (Fp), the magnitude of the real part of theimpedance (Zp), the resonant frequency of the imaginary part of theimpedance (F1), and the anti-resonant frequency of the imaginary part ofthe impedance (F2), signal magnitude (Z1) at the resonant frequency ofthe imaginary part of the impedance (F1), signal magnitude (Z2) at theanti-resonant frequency of the imaginary part of the impedance (F2), andzero-reactance frequency (Fz, frequency at which the imaginary portionof impedance is zero). Other spectral parameters may be simultaneouslymeasured using the entire impedance spectra, for example, quality factorof resonance, phase angle, and magnitude of impedance. Collectively,“spectral parameters” calculated from the impedance spectra (such asnon-resonance or resonance spectra), are called here “features” or“descriptors.” The appropriate selection of features is performed fromall potential features that can be calculated from spectra.Multivariable spectral parameters are described in U.S. Pat. No.7,911,345 entitled “Methods and systems for calibration of RFIDsensors,” which is incorporated herein by reference.

A resonance impedance or impedance may refer to measured sensorfrequency response from which the sensor spectral parameters areextracted.

Sensing materials and/or sensing films may include, but are not limitedto, materials deposited onto a transducer's electronics module, such aselectrodes of the CR or LCR circuit components or an RFID tag, toperform the function of predictably and reproducibly affecting theimpedance sensor response upon interaction with the environment. Forexample, a conducting polymer such as polyaniline changes itsconductivity upon exposure to solutions of different pH. When such apolyaniline film is deposited onto the CR or the LCR or RFID sensor, theimpedance sensor response changes as a function of pH. Thus, such as aCR or LCR or RFID sensor works as a pH sensor. When such a polyanilinefilm is deposited onto the CR or LCR or RFID sensor for detection in gasphase, the impedance sensor response also changes upon exposure to basic(for example, NH3) or acidic (for example, HCl) gases. Alternatively,the sensing film may be a dielectric polymer. Sensor films include, butare not limited to, polymer, organic, inorganic, biological, composite,and nano-composite films that change their electrical and or dielectricproperty based on the environment that they are placed in. Non-limitingadditional examples of sensor films may be a sulfonated polymer such asNafion, an adhesive polymer such as silicone adhesive, an inorganic filmsuch as sol-gel film, a composite film such as carbonblack-polyisobutylene film, a nanocomposite film such as carbonnanotube-Nafion film, gold nanoparticle-polymer film, metalnanoparticle-polymer film, electrospun polymer nanofibers, electrospuninorganic nanofibers, electrospun composite nanofibers, or films/fibersdoped with organic, metallorganic or biologically derived molecules andany other sensing material. In order to prevent the material in thesensor film from leaching into the liquid environment, the sensingmaterials are attached to the sensor surface using standard techniques,such as covalent bonding, electrostatic bonding, and other standardtechniques known to those of ordinary skill in the art. In addition, thesensing material has at least two temperature-dependent responsecoefficients related to temperature-dependent changes in materialdielectric constant and resistance of the sensing material.

Transducer and/or sensor may be used to refer to electronic devices suchas CR, LCR or RFID devices intended for sensing. Transducer can be adevice before it is coated with a sensing film or before it iscalibrated for a sensing application. A sensor may be a device typicallyafter it is coated with a sensing film and after being calibrated forthe sensing application.

FIG. 1 is a schematic diagram of a wireless sensor network (WSN) 100, inaccordance with an embodiment. The WSN 100 includes a remote system 108and one or more sensor nodes 102. Optionally, the WSN 100 may include aweather station 104. The weather station 104 may be configured toacquire one or more ambient parameters (e.g., wind direction and/orspeed, temperature, humidity, and/or the like) based on the environmentof the WSN 100. The weather station 104 may include an anemometer,thermometer, barometer, hygrometer, pyranometer, rain gauge, and/or thelike. For example, the weather station 104 may be configured to acquirea wind speed, a wind direction, temperature, and/or the like of ageographical area (e.g., a regional site 114) proximate to the sensornodes 102 of the WSN 100. The nodes 102 and/or the weather station 104may be communicatively coupled to the remote system 108 via one or morebi-directional communication links 110-113. Optionally, the data fromthe weather station 104 may be synchronized with responses of the sensornodes 102 to provide more accurate sensor readings of the environmentalparameters.

The remote system 108 is communicatively coupled to the sensor nodes 102via one or more bi-directional communication links 110-113. Thebi-directional communication links 110-113 may be based on one or morestandard wireless protocols such as Bluetooth Low Energy, Bluetooth,WiFi, 802.11, ZigBee, and/or the like. The bi-directional communicationlinks 110-113 may be configured to exchange data (e.g., environmentalparameters, ambient parameters, operational status, and/or the like)between components (e.g., node 102, Weather station 104, remote system108, and/or the like) of the WSN 100.

Optionally, the sensor nodes 102 may be connected wirelessly or wired tothe Internet of Things and/or to the Industrial Internet via a PREDIX™software platform (General Electric Company) for the use in assetoptimization, industrial automation, machine diagnostics, optimizationof industrial, healthcare, manufacturing and infrastructure managementprocesses, to monitor asset production performance with a view toidentifying trends, predicting outage, and other conditions.

Additionally or alternatively a WSN Gateway 106 may be communicativelyinterposed between the remote system 108 and one or more of the sensornodes 102 and/or weather station 104. For example, the WSN Gateway 106is configured to communicatively couple the nodes 102 and the weatherstation 104 together to form the regional site 114. The WSN Gateway 106may communicatively couple the regional site 114 to the remote system108 via the bi-directional communication link 113. It may be noted thatin various embodiments, the remote system 108 may be communicativelycoupled to a plurality of regional sites 114. For example, each of theregional sites 114 may correspond to different geographical locations.Additionally or alternatively, the regional sites 114 may correspond toan area proximate to a section of an industrial site and/or commercialsite, an exhaust outlet, and/or the like. Optionally, the WSN Gateways106 may be configured to bridge different wireless protocols. Forexample, the bi-directional communication links 110-112 within theregional site 114 may utilize a different wireless protocol relative tothe bi-directional communication link 113.

The weather station 104 may be a federal, state and/or private weatherstation located in general area of interest outside the area 114. Inthis case the bi-directional communication link 112 may be replaced witha one-way communication of data from the weather station to the WSNGateway 106 and/or to the remote system 108.

The remote system 108 may be a part of the Internet and/or otherremote-based net, server, database, cloud and/or any other source ofremote data storage and processing.

Optionally, the sensor nodes 102 of this invention may be combined withmobile robotic devices (e.g., for location and validation of pollution,homeland security threat, and other sources), GPS sub-systems, public orpersonal transportation vehicles for pollution and homeland securitythreat monitoring with a significant benefit of matching vehicle/sensormaintenance schedules.

Additionally or alternatively, the sensor nodes 102 may be implanted orincorporated in different objects, articles, items for real-timemonitoring of chemical, biological, and physical parameters.Non-limiting examples of implanting or incorporation of the sensor nodes102 into an industrial or consumer infrastructure or components mayinclude stationary industrial infrastructure, moving industrial outdoorsinfrastructure, industrial indoors infrastructure, urban outdoorsinfrastructure, urban indoors infrastructure, roads, buildings, bridges,vehicles, wind power turbines, aircraft engines, single-use and multipleuse bioprocess components, consumer products, home appliances, consumerappliances, sports equipment, laboratory equipment, laboratoryanalytical instrumentation, and/or the like.

FIG. 2 is a schematic diagram 200 of the sensor node 102 of oneembodiment of the WSN 100. The sensor node 102 includes a controllercircuit 210, a memory 204, a sensor 202, an environmental sensor 212, aradio frequency (RF) circuit 216, and an ambient power source 206.

The memory 204 is an electronic storage device configured to storeinformation acquired from the sensor 202 (e.g., an impedance spectrum, atransfer function, and/or the like), the environmental sensor 212,and/or the like. The contents of the memory 204 may be accessed by thecontroller circuit 210, the RF circuit 216, and/or the like. The memory204 may include protocol firmware that may be accessed by the controllercircuit 210. The protocol firmware may provide the wireless protocolsyntax for the controller circuit 210 to assemble data packets,establish the bi-directional communication links 111-112 based on thewireless protocol, partition data from the data packets, and/or thelike. The protocol syntax may include specifications on the structure ofpackets (e.g., frame size, packet specifications, appropriate number ofbits, frequency, and/or the like) that are received and/or transmittedby the sensor node 102. The memory 204 may include flash memory, RAM,ROM, EEPROM, and/or the like.

The controller circuit 210 is configured to control the operation of thesensor node 102 and obtains measurements representing environmental andambient parameters acquired by the sensor 202 and the environmentalsensor 212. In various embodiments, the controller circuit 210 may beconfigured to apply a stimulation waveform to the sensor 202. Thestimulation waveform may be an electrical stimulus configured to be asinusoidal waveform having an amplitude (e.g., voltage, current, and/orthe like) and a dynamic frequency. Optionally, the controller circuit210 may adjust the frequency of the stimulation waveform over time. Forexample, the controller circuit 210 may adjust the frequency of thestimulation waveform between frequencies of a resonate bandwidth of thesensor 202. In another example, the stimulation waveform may adjust thefrequency of the stimulation waveform between frequencies of a scanningbandwidth of the sensor 202. The scanning bandwidth includes a range offrequencies that are non-resonate frequencies of the sensor 202.Additionally or alternatively, the electrical stimulus may be configuredto have a static frequency. For example, the electrical stimulus mayhave frequency at and/or about a resonant frequency of the sensor 202.

The controller circuit 210 is configured to acquire an impedanceresponse of the sensor 202 in response to the stimulation waveform. Thecontroller circuit 210 may be embodied in hardware, such as a processor,controller, or other logic-based device, that performs functions oroperations based on one or more sets of instructions (e.g., software).The instructions on which the hardware operates may be stored on atangible and non-transitory (e.g., not a transient signal) computerreadable storage medium, such as the memory 204. Alternatively, one ormore of the sets of instructions that direct operations of the hardwaremay be hard-wired into the logic of the hardware.

The RF circuit 216 may be configured to handle and/or manage thebi-directional communication links between the sensor node 102 and theremote system 108, the WSN Gateway 106, and/or the like. The RF circuit216 is controlled by the controller circuit 210 and may support one ormore wireless communication protocols. For example, the wirelesscommunication protocols may include Bluetooth low energy, Bluetooth,ZigBee, WiFi, 802.11, and/or the like. Protocol firmware may be storedin the memory 204, which is accessed by the controller circuit 210. Theprotocol firmware provides the wireless protocol syntax for thecontroller circuit 210 to assemble data packets, establish one or morebi-directional communication links 110-111, and/or partition datareceived from other components of the WSN 100 (e.g., the remote system108, WSN Gateway 106, weather station 104, another sensor node 102,and/or the like).

The environmental sensor (e.g., environmental sensor suite) 212 may beconfigured to acquire ambient parameters (e.g., temperature, humidity,and/or the like) of the environment (e.g., not the analytes of interest)proximate to the sensor node 102 and/or exposed by the environmentalsensor 212. The environmental sensor 212 includes a thermistor, athermocouple, a humidity sensor, a photosensor, an anemometer, and/orthe like. The environmental sensor 212 may generate one or more sensormeasurement signals, which are obtained by the controller circuit 210.The sensor measurement signals may be a digital signal representing oneor more measurement values representing the one or more ambientparameters (e.g., temperature, humidity) acquired by the environmentalsensor 212. Additionally or alternatively, the sensor measurementsignals may be one or more analog signals having a predeterminedelectrical characteristic (e.g., frequency, amplitude, phase, and/or thelike) representing the one or more measurement values representing theone or more ambient parameters acquired by the environmental sensor 212.

The ambient power source 206 may be configured to generate electricalpower (e.g., current, voltage) for the one or more components of thesensor node 102. The ambient power source 206 may be an energy harvesterconfigured to generate electrical power derived from the ambientenvironment (e.g., sunlight, thermal energy, wind energy, kineticenergy, electromagnetic radiation, and/or the like) proximate to thesensor node 102. For example, the ambient power source 206 may include asolar panel (e.g., photovoltaic generator), a thermoelectric generator,a wind turbine, piezoelectric material, and/or the like. Additionally oralternatively, the ambient power source 206 may be electrically coupledto an electrical storage device (not shown), such as a battery,capacitor, and/or the like. For example, the electrical storage devicemay be configured to supplement and/or complement electrical powergenerated by the ambient power source 206 when the power generated bythe source 206 is deficient to power the components of the sensor node102.

Additionally or alternatively, the sensor node 102 may include a heater(not shown). The heater may be thermally coupled to the sensor 202 andis configured to generate thermal energy. For example, the heater mayinclude one or more heating elements configured to convert electricalpower (e.g., current, voltage) to generate thermal energy (e.g.,heater). The amount of thermal energy generated by the heater may bebased on instructions received by the controller circuit 210. Forexample, the heater may increase a temperature of the sensor 202 atleast 50 degrees Celsius above the ambient temperature measured by theenvironmental sensor 212.

The sensor 202 is configured to measure and/or detect a presence of oneor more analytes of interest within the ambient (e.g., in operationalcontact with the sensing material 214, proximate to, surrounding area,within a predetermined distance of a surface are of the sensing material214, and/or the like) environment of the sensor 202. For example, thesensor 202 may be a multivariable gas sensor. The sensor 202 includes atleast one pair of electrodes 208-209 and a sensing material 214. Theelectrodes 208-209 are conductors that are electrically coupled to thesensing material 214 and the controller circuit 210. For example, theelectrodes 208-209 are in contact with the sensing material 214. Theelectrodes 208-209 are configured to deliver the stimulation waveformgenerated by the controller circuit 210 to the electrodes 208-209 and tothe sensing material 214.

The sensing material 214 is configured to predictably and reproduciblyaffect and adjust the impedance of the sensor 214 in response to changesin the environment. For example, characteristics (e.g., magnitude of thereal part of the impedance, magnitude of the imaginary part of theimpedance, phase of the impedance, and/or the like) of the impedance ofthe sensing material 214 are adjusted based on a concentration,presence, and/or the like of the analyte of interest within the ambientenvironment of the sensor 202. The sensing material 214 is inoperational contact with the ambient environment. For example, at leasta portion of a surface area of the sensing material 214 is exposed toand/or in contact with the environment adjacent to the sensor 202, whichchanges an electrical property (e.g., inductance) of the sensingmaterial 214. The sensing material 214 may be a semiconducting polymer(e.g., polyaniline film, Nafion) and/or a dielectric polymer (e.g.,silicone adhesive). Additionally or alternatively, the sensing material214 may include organic, inorganic (e.g., sol-gel film), biological,composite film (e.g., polyisobutylene film), a nano-composite film(e.g., electrospun polymer nanofibers, gold nanoparticle-polymer film,metal nanoparticle-polymer film, electrospun polymer nanofibers,electrospun inorganic nanofibers, electrospun composite nanofibers),n-type oxide semiconductor, p-type oxide semiconductor, graphene, carbonnanotubes, and/or the like that are configured to change an electricaland/or dielectric property based on an environment exposed to thesensing material 214.

Additionally or alternatively, the sensing material 214 may be a metaloxide. For example, the sensing material 214 may be a single-metal oxidesuch as ZnO, CuO, CoO, SnO2, TiO2, ZrO2, CeO2, WO3, MoO3, In2O3, and/orthe like. In another example, the sensing material 214 may be aperovskite oxide having differently sized cations such as SrTiO3,CaTiO3, BaTiO3, LaFeO3, LaCoO3, SmFeO3, and/or the like. In anotherexample, the sensing material 214 may be a mixed metal oxide compositionsuch as CuO-BaTiO3, ZnO-WO3, and/or the like.

Optionally, the sensor 202 may be configured as a non-resonant circuit.Additionally or alternatively, the sensor 202 may be configured as aresonant circuit. For example, the sensor 202 may be a resonant circuitas described in the U.S. Patent Application entitled, “SYSTEMS ANDMETHODS FOR ENVIRONMENT SENSING” having docket number 285314-1US, whichis incorporated by reference in its entirety.

Optionally, sensor 202 may be configured to operate using any detectionprinciple that is applicable to measure the needed analyte or analytes.Non-limiting examples of such detection principles include non-resonantand resonant impedance sensors, electromechanical resonant sensors,field-effect transistor sensors, and photonic non-resonant and resonantsensors (such sensors may be multivariable sensors).

FIG. 3 is a schematic diagram 300 of the remote system 108 of oneembodiment of the WSN 100. The remote system 108 includes a controllercircuit 310, a memory 304, a display 306, a user interface 312, and aradio frequency (RF) circuit 316. The memory 304 is an electronicstorage device configured to store information acquired from one or moreregional sites 114 of the WSN 100. For example, the memory 304 mayinclude the environmental and ambient parameters received from one ormore sensor nodes 102, ambient parameters received from the weatherstation 104, and/or the like. The memory 204 may include protocolfirmware that may be accessed by the controller circuit 210. Theprotocol firmware may provide the wireless protocol syntax for thecontroller circuit 210 to assemble data packets, establish thebi-directional communication links 111-112 based on the wirelessprotocol, partition data from the data packets, and/or the like. Theprotocol syntax may include specifications on the structure of packets(e.g., frame size, packet specifications, appropriate number of bits,frequency, and/or the like) that are received and/or transmitted by thesensor node 102. The memory 204 may include flash memory, RAM, ROM,EEPROM, and/or the like. The contents of the memory 304 may be accessedby the controller circuit 310, the RF circuit 316, the display 306,and/or the like.

The RF circuit 316 may be configured to handle and/or manage thebi-directional communication links between the remote system 108 and thesensor nodes 102, the WSN Gateway 106, and/or the like. The RF circuit316 is controlled by the controller circuit 310 and may support one ormore wireless communication protocols. For example, the wirelesscommunication protocols may include Bluetooth low energy, Bluetooth,ZigBee, WiFi, 802.11, and/or the like. Protocol firmware may be storedin the memory 304, which is accessed by the controller circuit 310. Theprotocol firmware provides the wireless protocol syntax for thecontroller circuit 310 to assemble data packets, establish one or morebi-directional communication links 113 and/or partition data receivedfrom other components of the WSN 100 (e.g., the WSN Gateway 106, theweather station 104, sensor nodes 102, and/or the like).

The controller circuit 310 is configured to control the operation of theremote system 108. In various embodiments, the controller circuit 310 isconfigured to analyze the impedance responses received from the sensornodes 102 to determine the environmental parameters of the one or moreanalytes of interest. For example, the controller circuit 310 receivesthe impedance response of the sensor 202 measured by the controllercircuit 210 along the bi-directional communication links 110, 111, 113(FIG. 1). The controller circuit 310 analyzes the impedance response ofthe sensor 202 at frequencies that provide a linear response within apredetermined threshold (e.g., sufficiently linear) of the sensor 202 tothe one or more analytes of interest. The controller circuit 310 mayalso be configured to analyze the impedance response of the sensor 202at frequencies that provide a non-linear response, monotonic response ora non-monotonic response within a predetermined threshold of the sensor202 to the one or more analytes of interest. The controller circuit 310may be embodied in hardware, such as one or more processors, controller,or other logic-based device, that performs functions or operations basedon one or more sets of instructions (e.g., software). The instructionson which the hardware operates may be stored on a tangible andnon-transitory (e.g., not a transient signal) computer readable storagemedium, such as the memory 304. Alternatively, one or more of the setsof instructions that direct operations of the hardware may be hard-wiredinto the logic of the hardware.

Additionally or alternatively, the controller circuit 310 may beconfigured to analyze the impedance response of the sensor 202 at asingle frequency or multiple frequencies. Nonlimiting examples of thecontroller circuit 310 include application specific integrated circuits(ASICs) such as SL900A (AMS AG) or AD5933 (Analog Devices),micro-network analyzers such as a Vector Network Analyzer MiniVNA Pro(Mini Radio Solutions), and/or the like.

The controller circuit 310 may be configured to include an electricalcurrent sensor to monitor the current that is used by the sensor 202 anda gas flow sensor to monitor the air gas flow that is interacting withthe sensor 202.

The controller circuit 310 is operably coupled to the display 306 andthe user interface 312. The display 306 may include one or more liquidcrystal displays (e.g., light emitting diode (LED) backlight), organiclight emitting diode (OLED) displays, plasma displays, CRT displays,and/or the like. The display 306 may display one or more environmentalparameters of the analytes of interest based on impedance responsesreceived by the sensor nodes 102, geographical information of one ormore regional sites 114 of the WSN 100, components of a graphical userinterface, and/or the like received by the display 306 from thecontroller circuit 310.

The user interface 312 controls operations of the controller circuit 510and is configured to receive inputs from the user. The user interface312 may include a keyboard, a mouse, a touchpad, one or more physicalbuttons, and/or the like.

Optionally, the display 306 may be a touch screen display, whichincludes at least a portion of the user interface 312. For example, aportion of the user interface 312 may correspond to a graphical userinterface (GUI) generated by the controller circuit 310, which is shownon the display. The GUI may include one or more interface componentsthat may be selected, manipulated, and/or activated by the useroperating the user interface 312 (e.g., touch screen, keyboard, mouse).The interface components may be presented in varying shapes and colors,such as a graphical or selectable icon, a slide bar, a cursor, and/orthe like. Optionally, one or more interface components may include textor symbols, such as a drop-down menu, a toolbar, a menu bar, a titlebar, a window (e.g., a pop-up window) and/or the like. Additionally oralternatively, one or more interface components may indicate areaswithin the GUI for entering or editing information (e.g., patientinformation, user information, diagnostic information), such as a textbox, a text field, and/or the like.

FIG. 4 is a swim lane diagram of one embodiment of a method 400 fordetecting one or more analytes of interest within a WSN 100. The method400, for example, may employ or be performed by structures or aspects ofvarious embodiments (e.g., systems and/or methods) discussed herein. Forexample, the method 400 includes operations performed by and/or changesto the memory 204, 304, the controller circuit 210, 310, the sensor 202,and/or the like. In various embodiments, certain operations may beomitted or added, certain operations may be combined, certain operationsmay be performed simultaneously, certain operations may be performedconcurrently, certain operations may be split into multiple operations,certain operations may be performed in a different order, or certainoperations or series of operations may be re-performed in an iterativefashion. In various embodiments, portions, aspects, and/or variations ofthe method may be able to be used as one or more algorithms to directhardware to perform one or more operations described herein.

Beginning at 402, the controller circuit 210 of the sensor node 102 mayenter a sleep mode (e.g., low power mode, hibernation mode, stand-bymode). During the sleep mode, the controller circuit 210 may beconfigured to reduce an amount of current utilized by the sensor node102 relative to when not in the sleep mode. For example, during thesleep mode one or more components of the sensor node 102 do not receivepower, such as the environmental sensor 212 and/or the RF circuit 216.

At 404, the controller circuit 210 may determine whether a collectioninterval is reached. The collection interval may be a length of time thecontroller circuit 210 is configured to obtain one or more impedanceresponses from the sensor 202 and/or the sensor measurement signal fromthe environmental sensor 212. The collection interval may be interposedbetween sleep intervals corresponding to durations when the controllercircuit 210 enters a sleep mode. For example, the collection intervalmay be over a minute (e.g., range from 5-10 minutes) in length and isinterposed between sleep intervals (e.g., ranging from 1-4 hours inlength). It may be noted that the collection intervals and/or the sleepintervals may be similar to and/or the same for each sensor node 102within the regional site 114.

When the controller circuit 210 determines that the collection intervalis reached, at 406, the controller circuit 210 may apply a stimulationwaveform to the sensor 202. In connection with FIG. 5, the controllercircuit 210 may generate the stimulation waveform 504, which is receivedby the sensing material 214 utilizing the pair of electrodes 208-209 incontact with the sensing material 214. The stimulation waveform 504 isconducted through the electrodes 208-209 and is received by the sensingmaterial 214.

FIG. 5 is a graphical illustration 500 of a stimulation waveform 504applied to the sensing material 214 of the sensor 202. The stimulationwaveform 504 may be generated by the controller circuit 210. Thestimulation waveform 504 may be an electrical stimulus having anamplitude (e.g., voltage, current, and/or the like) and a dynamicfrequency. For example, the stimulation waveform 504 is shown plottedalong a horizontal axis 502 representing time. Over time, the controllercircuit 210 may adjust (e.g., increase, decrease) the frequency of thestimulation waveform 504. For example, as shown in FIG. 5, thecontroller circuit 210 may increase the frequency of the stimulationwaveform 504 along the axis 502 in a direction of an arrow 506. Invarious embodiments, the stimulation waveform 504 may be a chirp and/orsweep signal.

Optionally, a range of the frequencies of the stimulation waveform 504is adjusted by the controller circuit 210 based on a frequencybandwidth. The frequency bandwidth may be a defined range of frequenciescentered at a resonance frequency of the sensor 202 (e.g., configured toa part of a non-resonant or a resonant circuit). Additionally oralternatively, the range the frequency of the stimulation waveform 504is adjusted by the controller circuit 210 based on one or more scanningbandwidths. The scanning bandwidths may be a range of frequencies thatare non-resonant frequencies of the sensor 202. For example, thescanning bandwidths may be utilized by the controller circuit 210 whenthe sensor 202 is configured a part of a non-resonant circuit.

At 408, the controller circuit 210 measures an impedance response of thesensor 202. For example, the controller circuit 210 may receive ameasurement signal generated by the sensing material 214 from theelectrodes 208-209. The measurement signal is representative of animpedance response of the sensing material 214 in operational contactwith the ambient environment. For example, the measurement signal mayhave electrical characteristics (e.g., voltage, current, frequency,and/or the like), which is utilized by the controller circuit 210 tocalculate the impedance response.

FIGS. 6A-B illustrate a graphical illustration of measured responses600, 650 corresponding to a real and imaginary impedance responses 602,604, 652, 654 of the sensor 202, in accordance with an embodiment.

For example, the impedance response 600 (FIG. 6A) may represent theimpedance sensor response of the sensor 202 configured as a non-resonantsensor based on the stimulation waveform 504 generated by the controllercircuit 210. The impedance response 650 (FIG. 6B) may represent theimpedance sensor response of the sensor 202 configured as a resonantsensor based on a stimulation waveform generated by the controllercircuit 210. The impedance responses 600, 650 are measured by thecontroller circuit 210 based on a measurement signal. For example, thecontroller circuit 210 may receive the measurement signal from theelectrodes 208-209 in contact with the sensing material 214. Themeasurement signal is an electrical signal generated by the sensingmaterial 214 in response to the stimulation waveform 504 and the ambientenvironment exposed by the sensing material 214. The measurement signalis representative of the impedance response of the sensing material 214.For example, the measurement signal may have electrical characteristics(e.g., voltage, current, frequency, and/or the like), which may beutilized by the controller circuit 210 to calculate the impedanceresponses 600, 650. The impedance responses 600, 650 are divided intoreal portions 602, 652 corresponding to the real impedance, Zre(f) ofthe impedance responses 600, 650, and imaginary portions 604, 654 of animaginary impedance, Zim(f).

At 410, the controller circuit 210 may measure one or more sensormeasurement signals from the environmental sensor 212. For example, thecontroller circuit 210 may receive the sensor measurement signals fromthe environmental sensor 212. Based on the electrical characteristics(e.g., amplitude, voltage, frequency, bit sequence, and/or the like) ofthe sensor measurement signals the controller circuit 210 may determineone or more values representing the ambient parameters (e.g.,temperature, humidity, and/or the like).

At 412, the controller circuit 210 may calculate one or more ambientparameters (e.g., temperature, humidity, and/or the like) based on thesensor measurement signal. For example, based on a voltage of one of thesensor measurement signals the controller circuit 210 may determine atemperature measured by the environmental sensor 212.

At 414, the controller circuit 210 instruct the RF circuit 216 totransmit the impedance response and the one or more ambient parameters.For example, the controller circuit 210 may form a data packet based onthe wireless protocol stored in the memory 204. The data packet includesinformation associated with the impedance response (e.g., impedanceresponse 600, 650 of FIGS. 6A, 6B) that includes a real portion (e.g.,real portions 602, 652) of the impedance response corresponding to thereal impedance, Zre(f) of the impedance responses, and an imaginaryportion (e.g., imaginary portion 604, 654) of an imaginary impedance,Zim(f). Additionally or alternatively (e.g., in subsequent and/orpreceding data packet), the data packet includes information associatedwith the one or more ambient parameters (e.g., temperature, pressure,humidity, wind speed values) based on the sensor measurement signalsgenerated by the environmental sensor 212.

The data packets transmitted by the RF circuit 216 may further include atime stamp. The time stamp may represent a global time value of the WSN100 corresponding to when the data packet was transmitted by the RFcircuit 216. Additionally or alternatively, the time stamp may representwhen the impedance response and/or sensor measurement signals wasmeasured by the controller circuit 210. The global time value is basedon a network clock of the WSN 100. For example, the controller circuits210, 310 and the weather station 104 may each utilize a system clock.When the bi-directional communication links 110-113 are established, thecomponents of the WSN 100 may synchronize the system clocks within thecomponents of the WSN 100 to one of the system clocks designated as anetwork clock utilizing a clock synchronization protocol such as anetwork time protocol (NTP), a precision time protocol, based on globalposition system, and/or the like. For example, the controller circuits210, 310 and/or the weather station 104 may execute the NTP to align thesystem clocks of the sensor nodes 102 and the weather station 104 to thesystem clock of the remote system 108, which may be designated as thenetwork clock of the WSN 100.

During the collection interval, the controller circuit 210 may repeatoperations 406-414. In various embodiments, the controller circuit 210may continually repeat operations 406-414 until the collection intervalis terminated and/or a sleep interval is reached. A rate at which theoperations are performed may depend on a performance specification(e.g., processing speed) of the controller circuit 210. For example, thecontroller circuit 210 may be configured to continually collect and/ortransmit the impedance response and the one or more ambient parametersevery second.

At 416, the weather station 104 may obtain one or more ambientparameters (e.g., wind direction and/or speed, temperature, humidity,and/or the like). For example, the weather station 104 may determine awind direction and speed utilizing one or more sensors (e.g.,anemometer) of the weather station 104 of a geographical area proximateto the sensor nodes 102 of the WSN 100, such as the area formed by theregional site 114.

At 418, the weather station 104 transmits the one or more ambientparameters to the remote system 108. For example, the one or moreambient parameters may be included in a data packet based on thewireless protocol corresponding to the bi-directional communication link112. The data packets transmitted by the weather station 104 may furtherinclude a time stamp similar to and/or the same as the time stampincluded in the data packet transmitted by the RF circuit 216.Optionally, the weather station 104 may transmit the one or more ambientparameters continually not based on the collection interval, as shown inFIG. 4. For example, the weather station 104 may transmit data packetscontinually. Additionally or alternatively, the weather station 104 maytransmit the one or more ambient parameters periodically. For example,the weather station 104 may transmit the one or more ambient parametersduring the collection interval similar to and/or the same as the sensornodes 102 of the WSN 100. Additionally or alternatively, the weatherstation 104 may not be included within the WSN 100.

At 420, the RF circuit 316 may receive the measurements (e.g., theimpedance response, one or more ambient parameters) from the sensornodes 102 and the one or more ambient parameters from the weatherstation 104. For example, the RF circuit 316 may receive themeasurements from the sensor nodes 102 within via the bi-directionalcommunication link 110, 111, and 113. In another example, the RF circuit316 may receive the one or more ambient parameters from the weatherstation 104 via the bi-directional communication links 112-113.

The controller circuit 310 may align the received measurements and theone or more ambient parameters based on the time stamps included in thedata packets received from the sensor nodes 102 and the weather station104. By aligning the received measurements and the one or more ambientparameters, the controller circuit 310 may synchronize the data receivedfrom the sensor nodes 102 and the weather station 104 using the timestamps. For example, the controller circuit 310 may match the receivedmeasurements and the one or more ambient parameters having the same timestamps and/or time stamps within a predetermined threshold.

Additionally or alternatively, the alignment of the measurements (e.g.,the impedance response, one or more ambient parameters) from the sensornodes 102 and the one or more ambient parameters from the weatherstation 104 may be performed prior to being received by the RF circuit316. For example, the WSN Gateway 106 may be configured to synchronizethe data packets transmitted by the sensor nodes 102 and the weatherstation 104. The WSN Gateway 106 receives the data packets transmittedby the nodes 102 and the weather station 104 via the bi-directionalcommunication links 110-112. The WSN Gateway 106 may partition themeasurements and the one or more ambient parameters from received datapackets having the same time stamps and/or time stamps within apredetermined threshold to generate a new aligned payload. The WSNGateway 106 may form a new data packet having the aligned payload andtransmit the new data packet to the remote system 108 via thebi-directional communication link 113. Additionally or alternatively,the WSN Gateway 106 may group the received data packets based on thetime stamps to align the measurements and the one or more ambientparameters, which are transmitted successively to the remote system 108via the bi-directional communication link 113.

At 421, the controller circuit 310 may adjust the impedance responsesbased on the one or more ambient parameters (e.g., temperature,humidity). The controller circuit 310 may compare the ambient parameterswith an adjustment database stored in the memory 304. The adjustmentdatabase may include a plurality of candidate ambient parameters eachhaving corresponding impedance adjustments to be performed by thecontroller circuit 310 based on the ambient parameter. When thecontroller circuit 310 matches an ambient parameter to the adjustmentdatabase, the controller circuit 310 adjust the impedance responseaccording to the adjustment define within the adjustment database. Forexample, the controller circuit 310 may match an ambient parameterrepresenting a humidity measured by the environmental sensor 121 in theadjustment database. Based on the humidity, the controller circuit 310may adjust the impedance response by reducing or by increasing theimpedance according to the adjustment database.

At 422, the controller circuit 310 may analyze the impedance response ofthe sensing materials 214 at frequencies that provide a linear responseof the sensing materials 214. For example, the controller circuit 310may calculate one or more spectral parameters based on a real portion(e.g., Fp, Zp) and/or imaginary portion (e.g., F1, F2, Fz, Z1, Z2) ofthe impedance response. The controller circuit 310 may be configured toanalyze the spectral parameters that provide a linear response (e.g., asshown in FIGS. 7-9) of the sensing material 214 to the analyte ofinterest and at least partially reject effects of interference analytes(e.g., analytes that are not the analyte of interest). Optionally, theone or more spectral parameters calculated by the controller circuit 310may be based on a transfer function defining the linear relationshipbetween the impedance response and a parameter of the analyte ofinterest.

Additionally or alternatively, the sensor nodes 102 may be configured tooperate using any detection principle of a sensor that is applicable tomeasure the needed analyte or analytes of interest not utilizingnon-resonant and resonant impedance detection principles as shown inFIG. 4. For example, such detection principles may includeelectromechanical resonant sensors, field-effect transistor sensors,photonic non-resonant and resonant sensors, and/or the like. Optionally,such sensors may be multivariable sensors.

As a non-limiting example, in connection with FIGS. 7-8, are receivedimpedance responses of the sensing material 214 at differentconcentrations of the analyte of interest.

FIG. 7A is a graphical illustration 700 of a spectral parameter 702calculated by the controller circuit 310 of the remote system 108. Forexample, the controller circuit 310 may be an ASIC (e.g., AMS SL900A)with two inputs such as capacitance input and resistance input. Thespectral parameter 702 may represent ASIC counts when the sensor 202 isconnected to the controller circuit 310 at the resistance input plottedalong a horizontal axis 704 representing time. The sensor 202 wasexposed to different concentrations of the analyte of interest (e.g.,4.5 ppm, 9 ppm, 13 ppm, 18 ppm, 22 ppm) and a dry air in between theexposures over time to form peaks 710-714. The spectral parameter 702response based on the exposure to the different concentrations of theanalyte of interest (e.g., methane gas) are represented by a linearityof the peaks 710-714 of the spectral parameter 702. Each of the peaks710-714 may have an amplitude based on the concentration of the analyteof interest presented to the sensor 202. For example, the amplitude ofthe peak 710 is less than the amplitude of the peak 713 representing theconcentration of the analyte of interest of the peak 710 is less than atthe peak 713.

FIG. 7B is a graphical illustration 750 of the peak 710 of an embodimentof the spectral parameter 702. The peak 710 corresponds to the lowestconcentration of the analyte of interest (e.g., 4.5 ppm) of the spectralparameter 702 shown in FIG. 7B. The graphical illustration 750illustrates a high signal-to-noise of the sensor 202 response to thelowest analyte of interest corresponding to the peak 710. For example,the signal represented as an amplitude 751 of the peak 710 isdistinguishable from the regions 752 and 753 enclosing the peak 710having a signal-to-noise ratio greater than 1.

FIG. 8A is a graphical illustration 800 of a spectral parameter 802calculated by the controller circuit 310 of the remote system 108. Forexample, the controller circuit 310 may be an ASIC (e.g., AD5933). Thespectral parameter 802 may be a real part of impedance of the sensor 202as measured by the controller circuit 310 plotted along a horizontalaxis 804 representing time. The sensor 202 was exposed to differentconcentrations of the analyte of interest (e.g., 17 ppm, 34 ppm, 52 ppm,69 ppm, 86 ppm, 103 ppm, 121 ppm, 138 ppm, 155 ppm, 172 ppm, 190 ppm,207 ppm) and a dry air in between the exposures over time to form peaks810-821. The spectral parameter 802 response based on the exposure tothe different concentrations of the analyte of interest (e.g., methanegas) are represented by a linearity of the peaks 810-821 of the spectralparameter 802. Each of the peaks 810-821 may have an amplitude based onthe concentration of the analyte of interest exposed by the sensor 202.For example, the amplitude of the peak 810 is less than the amplitude ofthe peak 813 representing the concentration of the analyte of interestof the peak 810 is less than at the peak 813.

FIG. 8B is a graphical illustration 850 of the peak 810 of an embodimentof the spectral parameter 802. The peak 810 corresponds to the lowestconcentration of the analyte of interest (e.g., 17 ppm) of the spectralparameter 802 shown in FIG. 8B. The graphical illustration 850illustrates a high signal-to-noise of the sensor 202 response to thelowest analyte of interest corresponding to the peak 810. For example,the signal represented as an amplitude 851 of the peak 810 isdistinguishable from the regions 852 and 853 enclosing the peak 810having a signal-to-noise ratio greater than 1.

In connection with FIGS. 9A-B, a calibration curve 903 may be definedbased on peaks 960-964. For example, the controller circuit 310 mayanalyze a spectral parameter 952 of the impedance response of the sensor202 of the sensor node 102 having a linear response.

FIG. 9A is a graphical illustration 950 of the spectral parameter 952calculated by the controller circuit 310 of the sensor 202 of the sensornode 102. Optionally, the sensor 202 may be configured as a resonantsensor. The spectral parameter 902 is a peak frequency Fp plotted alonga horizontal axis 954 representing time. The sensor 202, operating inthe resonant mode, was exposed to different concentrations (e.g., 111ppm, 222 ppm, 444 ppm, 667 ppm, 889 ppm) of the analyte of interest anda dry air in between the exposures over time.

The spectral parameter 952 response is based on the exposure to thedifferent concentrations of the analyte of interest (e.g., methane gas)is represented by a linearity of the peaks 960-964 of the spectralparameter 952. Each of the peaks 960-964 may have an amplitude based onthe concentration of the analyte of interest presented to the sensor202. For example, the amplitude of the peak 960 is less than theamplitude of the peak 963 representing the concentration of the analyteof interest of the peak 960 is less than at the peak 963. In connectionwith FIG. 9B, a calibration curve 903 may be defined based on the peaks960-964.

FIG. 9B is a graphical illustration 900 of the concentration curve 903of the sensor 202 based on the spectral parameter 952 response shown inFIG. 9A. The concentration curve 903 is constructed from the spectralparameter 902, such as the peaks 960-964. For example, the concentrationcurve 903 is constructed from data points 908-912 based on theamplitudes of the peaks 960-964. It may be noted that the concentrationcurve 903 is linear (e.g., not exponential). This unexpected discoveryshows that the sensor 202 of the sensor node 102 produces a highlylinear response to response measurements of an analyte of interest(e.g., methane gas) of the spectral parameter 952.

The graphical illustration 900 represents the linear relationship ofcharacteristics of an impedance response of the sensor 202 andparameters of the analyte of interest, in accordance with an embodiment.The characteristics of the impedance response may correspond to thefrequencies of the real portion of the impedance response, which isplotted along a vertical axis 906. The parameters of the analyte ofinterest may correspond to the concentration of the analyte of interest(e.g., parts per million (ppm)) in the ambient environment of the sensor202. The graphical illustration 900 includes the plurality of datapoints 908-912. Each of the data points 908-912 may correspond tofrequencies of the real portion of the impedance responses at differentconcentrations of the analyte of interest. For example, data point 908may correspond to a concentration at 904 with the frequency at 905 ofthe real portion of the impedance response. In another example, the datapoint 909 may correspond to a concentration at 918 with the frequency at914 of the real portion of the impedance response.

The data points 908-912 define a linear response (e.g., not power-law)of the concentration curve 903 of the frequencies of the real portion ofthe impedance response of the sensor 202 at different concentrations.Based on the linear response of the concentration curve 903, thecontroller circuit 310 may define a transfer function of the sensor 202.

In connection with FIG. 10, the controller circuit 310 may analyze themagnitude of the real portion of the impedance response that includesmultiple analytes (e.g., water, methane, tetrahydrofuran, benzene, ethylacetate, ethanol, toluene, and/or the like). For example, a spectralparameter 1000 may be in response to the sensor 202 of the sensor nodes102 being exposed individually to different analytes (e.g., vapors) asseparate exposures with dry air interposed between the exposures of eachanalyte. It may be noted that the controller circuit 310 may analyzeadditional spectral parameters concurrently and/or simultaneously withthe each other. For example, the controller circuit 310 may analyze thefrequencies of the real portion of the impedance response concurrentlyand/or simultaneously with the impedance magnitudes of the real portionof the impedance response.

FIG. 10 is a graphical illustration of a spectral parameter 1000calculated from an impedance response received by the remote system 108from a sensor node 102, in accordance with an embodiment. The spectralparameter 1000 may correspond to impedance magnitude Zp calculated froma real portion of the resonant impedance response. The magnitudes of theimpedance Zp are plotted along a vertical axis 1002. The spectralparameter 1000 shown in FIG. 10 shows the sensor 202 has across-sensitivity to different analytes. For example, the spectralparameter based on the ambient environment in contact with the sensingmaterial 214 of the sensor 202, includes multiple response peaks1005-1011. Each of the peaks 1005-1011 may correspond to a differentanalyte (e.g., gas or vapor) detected within the ambient environment ofthe sensor 202. For example, one of the peaks 1005-1011 may correspondto water, methane, tetrahydrofuran, benzene, ethyl acetate, ethanol,toluene, and/or the like.

As depicted in FIG. 10, responses Zp to different gases or vapors havedifferent magnitudes. The controller circuit 310 may compare thefrequencies of the frequency peaks to an analyte parameter database todetermine which of the frequency peaks correspond to the analyte ofinterest. The analyte parameter database may be stored in the memory304. The analyte parameter database may include a plurality of analyteseach having corresponding spectral parameters. For example, the analyteparameter database may include a plurality of analytes withcorresponding real frequencies. The controller circuit 310 may identifythe analyte of interest within the analyte parameter database withcorresponding real frequencies that include the frequency at 1004. Thecontroller circuit 310 may determine that the frequency peak 1006 thatincludes the frequency at 1004 corresponds to the analyte of interest,and filter and/or reject the frequency peaks 1005, 1007-1011corresponding to interference and/or analytes not of interest.

Additionally or alternatively, in connection with FIG. 11, thecontroller circuit 310 may execute a multivariate analysis of theimpedance response received by the sensor nodes 102 to multiple analytesperformed using spectral parameters Fp, Zp, F1, F2, Z1, and Z2 andprocessing these outputs using a principal components analysis (PCA).Based on the PCA, the controller circuit 310 may eliminate the effectsof volatiles (e.g., analytes not the analyte of interest) and provide anaccurate response to the analyte of interest into its unique responsedirection to determine an environmental parameter of interest.

FIG. 11 is a graphical illustration 1100 of one embodiment of aprincipal component analysis of a plurality of spectral parameters. Forexample, the graphical illustration 1100 is calculated by the controllercircuit 310 by executing a PCA analysis of spectral parameters Fp, Zp,F1, F2, Z1, and Z2 calculated from an impedance response received by thesensor nodes 102. Based on the multiple outputs 1102-1109 of the PCAresponse, the controller circuit 310 may discriminate between differentanalytes utilizing its unique response direction. Each of the multipleoutputs 1102-1109 correspond to a different analyte. For example, theoutput 1102 may represent dry air (e.g., control having no analytes),the output 1103 may represent water, the output 1104 may representbenzene, the output 1105 may represent ethyl acetate, the output 1106represent tetrahydrofuran, the output 1107 may represent ethanol, theoutput 1108 may represent methane, and the output 1109 may representtoluene.

At 424, the controller circuit 310 may determine an environmentalparameter of interest based on the impedance response. For example, thecontroller circuit 310 may utilize the transfer function stored in thememory 304 to determine the environmental parameter (e.g.,concentration) of the one or more analytes of interest within theambient environment of the sensor nodes 102. The transfer function maybe stored in the memory 304 and utilized by the controller circuit 310to determine a characteristic (e.g., environmental parameter ofinterest) of the analyte of interest based on one or more spectralparameters calculated from the impedance response. The controllercircuit 310 may compare a spectral parameter (e.g., peak frequency)based on the impedance response to determine the environmental parameterof interest (e.g., concentration of the analyte of interest). Forexample, the controller circuit 310 may determine a peak frequency basedon the impedance response received by the sensor node 102 at 916 of FIG.9. Based on the peak frequency at 916 of the concentration curve 903,the controller circuit 310 may determine the environmental parameter ofinterest, such as the concentration, is at 920. Additionally oralternatively, the controller circuit 310 may determine theenvironmental parameters based on the impedance response based on adirection and/or position utilizing the PCA as shown in FIG. 11.

At 426, the controller circuit 310 may determine if the environmentalparameter of interest (e.g., concentration) is above a predeterminedresponse threshold. The predetermined response threshold may be based ona value of the environmental parameter of interest representing achemical or physical hazard, such as corresponding to a leak within theremote site 114, and/or the like. For example, the controller circuit310 may compare the environmental parameter of interest determined at424 with the predetermined response threshold.

FIG. 12 are graphical illustrations of an embodiment of an impedanceresponse (e.g., 1201-1202) to an analyte and response to an ambientparameter 1203 of one of the sensor nodes 102 of the WSN system 100. Forexample, the impedance response may include a real part 1201 and animaginary part 1202 of the impedance response based on a stimulationwaveform generated by the controller circuit 210. The ambient parameter1203 shown in the graphical illustration 1203 may have been acquired bythe environmental sensor 212 of the sensor node 102. The impedanceresponses 1201-1202 and the ambient parameters 1203 are plotted over ahorizontal axis 1204 representing time. The sensor node 102corresponding to the impedance response (e.g., the real and imaginarypart 1201-1202) may have been configured to detect an analyte ofinterest, such as methane. The impedance response represents the sensornode 102 periodically being exposed to increasing concentrations of theanalyte of interest (e.g., 555.6 ppm, 1111 ppm, 1667 ppm, 2222 ppm, and2778 ppm) represented as peaks 1210, 1215 of the impedance responses1201-1202. Interposed between the exposures of the analyte of interest,the sensor nodes 102 were exposed to increasing concentrations of watervapor as an interferent (e.g., 20 and 40 percent of relative humidity)represented as peaks 1214, 1217, and 1220 with dry air between theexposures of the water vapor.

Additionally or alternatively, the real part 1201 and the imaginary part1202 of the impedance response may be based on stimulation waveforms atdifferent frequencies (e.g., at 406 of FIG. 4). For example, the realpart 1201 may be based on a stimulation waveform having a frequency of90 kHz, and the imaginary part 1202 may be based on a stimulationwaveform having a frequency of 70 kHz. The peaks 1210 have slightlynon-linear response 1212 and the peaks 1216 have a linear response 1215corresponding to the increase in concentration of the analyte ofinterest. Additionally or alternatively, the impedance response may beaffected by the frequency of the stimulation waveform. For example, thereal part 1201 of the impedance response of the sensor node 102 to theanalyte of interest (e.g., methane) may be slightly nonlinear at 90 kHzwith a relatively large response to the water vapor, represented by thepeaks 1214. In another example, the imaginary part 1202 of impedanceresponse of the sensor node to the analyte of interest (e.g., methane)may be linear at 70 kHz with a relatively small response to water vapor,represented by the peaks 1217.

The ambient parameter 1203 may represent a humidity proximate to thesensor node 102 generating the impedance response. For example, theenvironmental sensor 212 may be configured to measure a humidity. Theambient parameter 1203 includes a series of peaks 1220 corresponding toincreases in water vapor concentrations of 20 and 40 percent of relativehumidity proximate to the sensor node 102.

FIG. 13A-B are graphical illustrations 1300, 1350 of impedance responsesof embodiments of the sensor nodes 102 of the WSN system 100. Each ofthe impedance responses represent a response to a concentration of oneor more analytes of interest (e.g., methane) at different distances froma leak or source of the analyte of interest (e.g., exhaust port) and thesensor nodes 102. The impedance responses are plotted along a horizontalaxis 1302 representing time. The sensor nodes 102 are positioned suchthat positions of the source of the analyte of interest relative to thesensor nodes 102 are configured such that wind may transport the analyteof interest (e.g., leaked methane) in a direction toward the sensor node102. For example, the graphical illustration 1300 of FIG. 13A may depictthe impedance response of the sensor node 102 at a distance ofninety-one meters from the leak or source of the analyte of interest.The impedance response includes a series of peaks 1304 corresponding toa detection of the analyte of interest during dynamic wind changes.

In another example, the graphical illustration 1350 of FIG. 13B maydepict the impedance response of the sensor node 102 at a distance oftwo hundred thirteen meters from the leak or source of the analyte ofinterest. The impedance response includes a series of peaks 1352corresponding to a detection of the analyte of interest during dynamicwind changes. A technical effect of the impedance response shown in FIG.13A and FIG. 13B illustrates an ability of the sensor node 102, and thesensor 202, to detect one or more analytes of interest at varyingdistances and to communicate wirelessly the data to the remote system108.

FIG. 14 are graphical illustrations of an embodiment of an impedanceresponse 1401 and ambient parameters 1402-1403 of the sensor node 102 ofthe WSN system 100. The impedance response 1401 and the ambientparameters 1402-1403 may be received by the WSN Gateway 106. Also, theimpedance response 1401 and the ambient parameters 1402-1403 may bereceived by the remote system 108 via one or more bi-directionalcommunication links (e.g., the bi-directional communication links110-111, 113). The impedance response 1401 and the ambient parameters1402-1403 are plotted over a horizontal axis 1404 representing time. Forexample, the impedance response 1401 of the sensor node 102 upondetection of the analyte of interest may be over several hoursrepresenting a field measurement. The ambient parameter 1402-1403 mayhave been acquired by the environmental sensor 212 of the sensor node102. The ambient parameter 1402 represents temperature proximate to thesensor node 102, and the ambient parameter 1403 represents humidity. Forexample, the ambient parameter 1402 illustrates that the ambienttemperature proximate to the sensor nodes 102 increased fromapproximately 25 degrees Celsius to approximately 35 degrees Celsiusduring the field measurement. In another example, the ambient parameter1403 illustrates the ambient relative humidity was changing fromapproximately 83 percent to approximately 47 percent during the fieldmeasurement.

The sensor node 102 corresponding to the impedance response 1401 mayhave been configured to detect an analyte of interest, such as methane.The sensor node 102 corresponding to the impedance response 1401 waspositioned such that a position of the source of the analyte of interestrelative to the sensor node 102 is configured such that wind maytransport the analyte of interest (e.g., leaked methane) in a directiontoward the sensor node 102. For example, a series of peaks 1410represent concentrations of the analyte of interest detected by thesensor node 102. A technical effect of the impedance response 1401illustrates a stability of the response of the sensor node 102 undervariable ambient temperature and humidity represented by the ambientparameters 1402-1403.

In an embodiment a sensor system is provided. The system includes asensor node having a sensor. The sensor includes a sensing materialconfigured to be in contact with an ambient environment. The systemincludes a remote system having a communication circuit and a controllercircuit. The communication circuit is configured to be wirelesslycommunicatively coupled to the sensor node. The controller circuitelectrically coupled to the communication circuit. The controllercircuit configured to receive an impedance response of the sensingmaterial and analyze the impedance response of the sensing material atfrequencies that provide a linear response of the sensing material to ananalyte of interest and at least partially reject effects ofinterferences.

Optionally, the sensor node includes at least one pair of electrodes incontact with the sensing material, and a controller circuit of thesensor node electrically coupled to the at least one pair of electrodes.The controller circuit of the sensor node may be configured to generatea stimulation waveform for application to the sensing material of thesensor via the at least one pair of electrodes. Additionally oralternatively, the controller circuit of the sensor node is configuredto receive an electrical signal from the at least one pair of electrodesrepresentative of the impedance response of the sensing material.

Optionally, the sensor node includes a communication circuit configuredto wirelessly transmit the impedance response of the sensing material tothe remote system.

Optionally, the sensor node includes an ambient power source configuredto generate electrical power derived from the ambient environmentproximate to the sensor node.

Optionally, the sensor node further includes an environmental sensorconfigured to acquire one or more ambient parameters of the ambientenvironment proximate to the sensor node. The controller circuit mayfurther be configured to receive the one or more ambient parameters.Additionally or alternatively, the one or more ambient parametersinclude at least one of a temperature, pressure, or humidity.Additionally or alternatively, the controller circuit is furtherconfigured to adjust the impedance response based on the one or moreambient parameters.

Optionally, the sensor node further includes an electrical currentsensor or a gas flow sensor.

Optionally, the sensor node further includes a controller for operatingthe sensor at a temperature of at least 200 degrees Celsius.

Optionally, the system includes a weather station configured to acquireone or more ambient parameters based on an ambient environment of awireless sensor network. The controller circuit may be configured toalign the one or more ambient parameters with the impedance responsebased on time stamps.

Optionally, at least one of the communication circuit of the remotesystem or a communication circuit of the sensor node is configured tocommunicatively couple the sensor node with the remote system utilizinga plurality of bi-directional communication links.

Optionally, the controller circuit is configured to calculate one ormore spectral parameters based on the impedance response and to alert auser when an environmental parameter of interest of the analyte ofinterest is above a predetermined threshold.

In an embodiment a sensor node is provided. The sensor node includes asensor having a sensing material and at least one pair of electrodes incontact with the sensing material. The sensing material configured to bein contact with an ambient environment. The sensor node includes acommunication circuit configured to be communicatively coupled to aremote system. The sensor node includes a controller circuitelectrically coupled to the at least one pair of electrodes. Thecontroller circuit is configured to generate a stimulation waveform forapplications to the sensing material of the sensor via the at least onepair of electrodes. The controller circuit is configured to receive anelectrical signal from the at least one pair of electrodesrepresentative of an impedance response of the sensing material. Thecontroller circuit is further configured to control the communicationcircuit to transmit the impedance response to the remote system.

Optionally, the sensor node includes an ambient power source configuredto generate electrical power derived from the ambient environmentproximate to the sensor node.

Optionally, the controller circuit is configured to generate thestimulation waveform during a collection interval, the collectioninterval is interposed between a sleep mode. Additionally oralternatively, during the sleep mode the controller circuit isconfigured to reduce an amount of current utilized by the sensor noderelative to the collection interval.

Optionally, the sensor node further includes an electrical currentsensor or a gas flow sensor.

Optionally, the controller circuit is configured to operate the sensornode at a temperature of at least 200 degrees Celsius.

Optionally, the sensor node further includes an environmental sensorconfigured to acquire one or more ambient parameters of the environmentproximate to the sensor node, and the controller circuit is furtherconfigured to receive the one or more ambient parameters. Additionallyor alternatively, the one or more ambient parameters include at leastone of a temperature, pressure, or humidity. Additionally oralternatively, the impedance response of the sensing material ismeasured at one or more frequencies. Additionally or alternatively, theimpedance response of the sensing material is measured using anapplication specific integrated circuit.

Optionally, the sensor node is connected wirelessly or wired to theInternet of Things and/or to the Industrial Internet via a PREDIX™software platform for the use in asset optimization, industrialautomation, machine diagnostics, optimization of industrial, healthcare,manufacturing and infrastructure management processes, to monitor assetproduction performance with a view to identifying trends, predictingoutage, and other conditions.

In an embodiment a method (e.g., for detecting one or more analytes ofinterest) is provided. The method includes receiving a plurality ofimpedance responses and one or more ambient parameters from a pluralityof sensor nodes. Each impedance response is representative of a sensingmaterial of a sensor node in operational contact with an ambientenvironment. The method includes adjusting the plurality of impedanceresponses based on the one or more ambient parameters and analyzing theplurality of impedance responses at frequencies that provide a linearresponse of the sensing material to an analyte of interest and at leastpartially reject effects of interferences.

Optionally, the method includes aligning the one or more ambientparameters with the plurality of impedance responses based on timestamps.

As used herein, the terms “module”, “system,” “device,” “circuit,” or“unit,” may include a hardware and/or software system and circuitry thatoperates to perform one or more functions. For example, a module, unit,device, circuit, or system may include a computer processor, controller,or other logic-based device that performs operations based oninstructions stored on a tangible and non-transitory computer readablestorage medium, such as a computer memory. Alternatively, a module,unit, device, circuit, or system may include a hard-wired device thatperforms operations based on hard-wired logic and circuitry of thedevice. The modules, units, circuits, or systems shown in the attachedfigures may represent the hardware and circuitry that operates based onsoftware or hardwired instructions, the software that directs hardwareto perform the operations, or a combination thereof. The modules,systems, devices, circuits, or units can include or represent hardwarecircuits or circuitry that include and/or are connected with one or moreprocessors, such as one or computer microprocessors.

As used herein, the terms “software” and “firmware” are interchangeableand include any computer program stored in memory for execution by acomputer, including RAM memory, ROM memory, EPROM memory, EEPROM memory,and non-volatile RAM (NVRAM) memory. The above memory types areexemplary only and are thus not limiting as to the types of memoryusable for storage of a computer program.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventivesubject matter without departing from its scope. While the dimensionsand types of materials described herein are intended to define theparameters of the inventive subject matter, they are by no meanslimiting and are exemplary embodiments. Many other embodiments will beapparent to one of ordinary skill in the art upon reviewing the abovedescription. The scope of the inventive subject matter should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Moreover, in the following claims, the terms “first,”“second,” and “third,” etc. are used merely as labels, and are notintended to impose numerical requirements on their objects. Further, thelimitations of the following claims are not written inmeans-plus-function format and are not intended to be interpreted basedon 35 U.S.C. § 112(f), unless and until such claim limitations expresslyuse the phrase “means for” followed by a statement of function void offurther structure.

This written description uses examples to disclose several embodimentsof the inventive subject matter, including the best mode, and to enableone of ordinary skill in the art to practice the embodiments ofinventive subject matter, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe inventive subject matter is defined by the claims, and may includeother examples that occur to one of ordinary skill in the art. Suchother examples are intended to be within the scope of the claims if theyhave structural elements that do not differ from the literal language ofthe claims, or if they include equivalent structural elements withinsubstantial differences from the literal languages of the claims.

The foregoing description of certain embodiments of the presentinventive subject matter will be better understood when read inconjunction with the appended drawings. To the extent that the figuresillustrate diagrams of the functional blocks of various embodiments, thefunctional blocks are not necessarily indicative of the division betweenhardware circuitry. Thus, for example, one or more of the functionalblocks (for example, processors or memories) may be implemented in asingle piece of hardware (for example, a general-purpose signalprocessor, microcontroller, random access memory, hard disk, or thelike). Similarly, the programs may be stand alone programs, may beincorporated as subroutines in an operating system, may be functions inan installed software package, or the like. The various embodiments arenot limited to the arrangements and instrumentality shown in thedrawings.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or operations, unless such exclusion is explicitlystated. Furthermore, references to “one embodiment” of the presentinvention are not intended to be interpreted as excluding the existenceof additional embodiments that also incorporate the recited features.Moreover, unless explicitly stated to the contrary, embodiments“comprising,” “comprises,” “including,” “includes,” “having,” or “has”an element or a plurality of elements having a particular property mayinclude additional such elements not having that property.

What is claimed is:
 1. A sensor system comprising: a sensor node havinga first sensor and an environmental sensor, the first sensor having asensing material configured to be in contact with an ambientenvironment, the environmental sensor configured to acquire one or moreambient parameters of the ambient environment; and a remote systemhaving a communication circuit and a controller circuit, thecommunication circuit configured to be wirelessly communicativelycoupled to the sensor node, the controller circuit electrically coupledto the communication circuit, the controller circuit configured to:receive an impedance response of the sensing material; receive the oneor more ambient parameters; and analyze the impedance response of thesensing material at frequencies that one or more of: provide a linearresponse of the sensing material to an analyte of interest, or at leastpartially reject one or more effects of interferences.
 2. The sensorsystem of claim 1, wherein the controller circuit is configured toanalyze the impedance response of the sensing material at thefrequencies that provide the linear response of the sensing material tothe analyte of interest.
 3. The sensor system of claim 1, wherein thecontroller circuit is configured to analyze the impedance response ofthe sensing material at the frequencies that at least partially rejectthe one or more effects of interferences.
 4. The sensor system of claim1, wherein the controller circuit is configured to receive the impedanceresponse of the sensing material using an application specificintegrated circuit sensor.
 5. The sensor system of claim 1, wherein thesensor node includes at least one pair of electrodes in contact with thesensing material, and a controller circuit of the sensor nodeelectrically coupled to the at least one pair of electrodes, thecontroller circuit of the sensor node configured to generate astimulation waveform for application to the sensing material of thefirst sensor via the at least one pair of electrodes.
 6. The sensorsystem of claim 1, wherein the sensor node includes an ambient powersource configured to generate electrical power derived from the ambientenvironment proximate to the sensor node.
 7. The sensor system of claim1, wherein the one or more ambient parameters include at least one of atemperature, pressure, or humidity.
 8. The sensor system of claim 1,wherein the controller circuit is further configured to adjust theimpedance response based on the one or more ambient parameters.
 9. Asensor system comprising: a sensor node having a sensor that includes asensing material configured to be located in a geographic area beingmonitored by the sensor node; and a remote system having a communicationcircuit and a controller circuit, the communication circuit configuredto be wirelessly communicatively coupled to the sensor node, thecontroller circuit electrically coupled to the communication circuit,the controller circuit configured to: receive an impedance response ofthe sensing material while the sensing material is located in thegeographic area; and detecting a presence of an analyte of interest inthe geographic area being monitored by analyzing the impedance responseof the sensing material at frequencies that provide a linear response ofthe sensing material to the analyte of interest and that at leastpartially reject one or more effects of interferences.
 10. The sensorsystem of claim 9, wherein the controller circuit is configured toreceive the impedance response of the sensing material using anapplication specific integrated circuit sensor.
 11. The sensor system ofclaim 9, wherein the sensor node includes at least one pair ofelectrodes in contact with the sensing material, and a controllercircuit of the sensor node electrically coupled to the at least one pairof electrodes, the controller circuit of the sensor node configured togenerate a stimulation waveform for application to the sensing materialof the sensor via the at least one pair of electrodes.
 12. The sensorsystem of claim 9, wherein the sensor node includes an ambient powersource configured to generate electrical power derived from an ambientenvironment proximate to the sensor node.
 13. The sensor system of claim9, wherein the sensor of the sensor node is a first sensor, and thesensor node also includes an environmental sensor configured to senseone or more parameters of an ambient environment of the geographic area,wherein the controller circuit is configured to detect the presence ofthe analyte of interest based on the impedance response of the sensingmaterial and based on the one or more parameters of the ambientenvironment.
 14. The sensor system of claim 13, wherein the one or moreambient parameters include at least one of a temperature, pressure, orhumidity.
 15. The sensor system of claim 13, wherein the controllercircuit is further configured to adjust the impedance response based onthe one or more ambient parameters.
 16. A method comprising: acquiringone or more ambient parameters of an ambient environment with which asensing material of a sensor in a sensor node is in contact; obtainingan impedance response of the sensing material of the sensor; analyzingthe impedance response of the sensing material at frequencies that oneor more of: provide a linear response of the sensing material to ananalyte of interest, or at least partially reject one or more effects ofinterferences.
 17. The method of claim 16, wherein the impedanceresponse is obtained by generating a stimulation waveform forapplication to the sensing material of the sensor via at least one pairof electrodes.
 18. The method of claim 16, further comprising:generating electrical power derived from the ambient environment forpowering the sensor node.
 19. The method of claim 16, wherein the one ormore ambient parameters include at least one of a temperature, pressure,or humidity.
 20. The method of claim 16, further comprising: adjustingthe impedance response based on the one or more ambient parameters.